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Mawass, W.

Publications and source records attributed to Mawass, W..

2 recordsLinked to original sources

Extinction vortices are driven more by a shortage of beneficial mutations than by deleterious mutation accumulation

Habitat loss contributes to extinction risk in multiple ways. Genetically, small populations can face an "extinction vortex" -- a positive feedback loop between declining fitness and declining population size. Two distinct genetic mechanisms can drive a long-term extinction vortex: i) ineffective selection in small populations allows deleterious mutations to fix, driving "mutational meltdown", and ii) smaller populations generate fewer beneficial mutations essential for long-term adaptation, a mechanism we term "mutational drought". To determine their relative importance, we ask whether, for a population near its critical size for persistence, changes in population size have a larger effect on the beneficial vs. deleterious component of fitness flux. In stable environments, we find that mutational drought is nearly as significant as mutational meltdown. Drought is more important than meltdown when populations must also adapt to a changing environment, unless the beneficial mutation rate is extremely high. Linkage disequilibria from background selection under realistically high deleterious mutation rates modestly increase the importance of mutational drought. Long-term conservation efforts should consider adaptive potential, not just deleterious load.

evolutionary biology↗

Assessing the impact of pedigree quality on the validity of quantitative genetic parameterestimates

Investigating the evolutionary dynamics of complex traits in nature requires the accurate assessment of their genetic architecture. Using a quantitative genetic (QG) modeling approach (e.g., animal model), relatedness information from a pedigree combined with phenotypic measurements can be used to infer the amount of additive genetic variance in traits. However, pedigree information from natural systems is not perfect and might contain errors or be of low quality. Published sensitivity analyses revealed a limited impact of expected error rates on parameter estimates. However, natural systems will differ in many respects (e.g., mating system, data availability, pedigree structure), thus it can be inappropriate to generalize outcomes from one system to another. French-Canadian (FC) genealogies are extensive and deep-rooted (up to 9 generations in this study) making them ideal to study how the quality and properties (e.g., errors, completeness) of pedigrees affect QG estimates. We conducted simulation analyses to infer the reliability of QG estimates using FC pedigrees and how it is impacted by genealogical errors and variation in pedigree structure. Broadly, results show that pedigree size and depth are important determinants of precision but not of accuracy. While the mean genealogical entropy (based on missing links) seems to be a good indicator of accuracy. Including a shared familial component into the simulations led to on average a 46% overestimation of the additive genetic variance. This has crucial implications for evolutionary studies aiming to estimate QG parameters given that many traits of interest, such as life history, exhibit important non-genetic sources of variation.

evolutionary biology↗